Optimization of a Process for the Enzymatic Extraction of Nutrient Enriched Bael Fruit Juice Using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
Akshay Sonawane, Sumit Sudhir Pathak, Rama Chandra Pradhan
Abstract
Bael fruit pulp was treated with pectinase enzyme for the easy extraction and high recovery of juice with nutritive properties. The response surface methodology (RSM) and artificial neural network (ANN) modeling were carried out on runs of Box-Behnken design of three variables for the optimization process and identification of the best modeling method. Pectinase concentration of 0.22 g/100 g of pulp, the temperature of 46.20°C, and time of 6.35 hours was found out to be RSM optimized value of variables whereas pectinase concentration of 0.18 g/100 g of pulp, the temperature of 46.81°C and time of 6.09 hours was obtained to be ANN-GA optimized value of variables. From the values of the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE), ANN was found out to be better than RSM. This research would help in the commercial production of bael fruit juice on an industrial scale.